Electric motors and electric generators (i.e. electric machines) have been extensively utilized in critical service applications, such as electric vehicles for example. As such, time-demanding electric motor/generator control information and fault symptoms must be precisely monitored and adjusted at the motor to ensure its continued safe and secure operation. One fault diagnosis method that has been investigated to identify the indication of incipient, or initial, failure symptoms of electric machines, so as to prevent their catastrophic failure is referred to as Motor Current Signature Analysis (MCSA).
MCSA utilizes a motor current signal that is applied to the electric machine, which is readily available from a motor control service routine without the need of extra hardware or design cost. The motor current signal provides information regarding a vibration signature with respect to error rate under different operating conditions of the electric machine, such as speed variation and load torque variation. This vibration signature inherently contains valuable information regarding the various health status conditions of the electric machine, and the defined harmonics of the signature are utilized as health status indicators to diagnose various faults in the electric machine.
In traditional MCSA diagnosis, the fault conditions of an induction motor, or other electric machine, are analyzed to identify abnormal harmonic modulation at specific characteristic fault frequencies in a motor current signal spectrum. Fast Fourier Transform (FFT) has been utilized to perform the motor current spectrum analysis by assuming stationary operation of the motor or electric machine. In addition, a Short Time Fourier Transform (STFT) has also been used by assuming short-time steady state operation of the motor, and by sacrificing the resolution of the MCSA analysis. STFT has also been utilized to detect the eccentricity of a motor while an electric vehicle is in an idle mode. In addition, without applying restrictions on the operating point of the electric machine, a high-speed Digital Signal Processor (DSP) and sophisticated signal processing have been utilized to resolve non-linear signal distortion. Alternative time-frequency analysis techniques have also achieved acceptable performance by applying the Zhao-Atlas-Marks distribution in performing the subject analysis. In addition, other time-frequency analysis methods, such as adaptive statistical time-frequency distributions, and Wigner-Ville distributions, have also achieved successful performance; however they require overwhelming complexity and memory occupancy capacity. In a majority of the abovementioned methods, the operating point limitations of the electric motor, and other electrical machines, as well as the large computation complexity requirements of diagnostic signal processing has been problematic.
Advanced signal processing has been widely utilized for noise cancelation and identifying non-linear characteristic fault signatures. In addition, different probabilistic models, high-resolution frequency analysis and time-frequency analysis algorithms have been applied to obtain reliable fault signatures. A statistics-based Welch's periodogram of the stator currents of an electric motor has also been utilized for fault indication. The probability of the occurrence of abnormal conditions and presence of a fault is also confirmed through Bayesian estimation algorithms. Depending on the type of fault, different statistical methods have been employed to process/analyze fault signatures. For example, advanced motor current signal analysis has been utilized to diagnose roller bearing faults based on externally induced vibration and has been used to diagnose demagnetization faults of Permanent Magnet Synchronous Motors (PMSM). Electrical fault detection has also been carried out in three-phase wound-rotor induction machines (WRIM), while open-loop physics-based back electromotive force (EMF) signals have been measured for the diagnosis of inter-turn phase winding faults of PMSMs; and hierarchical neural network structures have been used to carry out statistical time feature analysis of vibration signals from a bearing to diagnose faults.
However the fundamental limitation of the MCSA diagnosis process is its inherent vulnerability to noise and interference, as MCSA relies on an extremely small fault frequency. That is, small frequency tone detection in the whole motor current spectrum is easily distorted by interference from harsh industrial noise in the surrounding environment of the electric machine, as well as the low quality of power applied to operate the motor, and the transient operation of the motor.
One popular approach for robust signal detection, including that used in the information technology industry (IT), is the use of not only one frequency tone, but the use of coded multi-frequency signal patterns. In addition, multi-frequency based signal detection processes have been widely adopted in wireless communication systems to implement a robust signal acquisition process that is capable of effective operation under harsh industrial noise, as well as in the presence of frequency disturbances, and frequency fading, etc. The design of a preamble signal, which is used for critical communication channel estimation and identification has been done through information theory to fully utilize the frequency distribution pattern information. Constant amplitude zero autocorrelation (CAZAC) sequence, Golay sequence, and a pseudo-noise (PN) sequence are examples that use 2nd, 3rd, and 4th generation wireless communication (code division multiple access (CDMA)) or orthogonal frequency division multiple access (OFDM) system. Signal detection techniques utilizing frequency distribution patterns have allowed robust communication to be possible under harsh industrial noise conditions because a fixed frequency-pattern can be discriminated from random noise patterns with considerably high probability.
Therefore, there is a need for a system and method for iterative condition monitoring and fault diagnosis of an electric machine that evaluates patterns of multiple fault signatures of a motor current signal applied to an electric machine that are spread over the wide motor current spectrum in a harsh industrial environment that is subject to electromagnetic noise, while rejecting signal distortion conditions, harsh noise, and interference from the dynamic operation of the electric motor. Furthermore, in modern energy conversion systems, especially in critical service applications that are subject to harsh industrial applications, the low signal-to-noise ratio (SNR) of the motor current signal makes identifying a reliable fault indication a challenging issue. As such, there is a need for a fault diagnosis signal processing scheme that is able to provide predictable and reliable fault diagnosis performance, while minimizing false alarm, and miss alarm rates. In addition, there is a need for a fault diagnosis scheme that is able to provide continuous monitoring independently from the operating point of an electric machine. Furthermore, there is a need in the art for a fault diagnosis system that simultaneously minimizes the false alarm and miss alarm rate under harsh noise conditions.